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Build a Medical RAG App using BioMistral, Qdrant, and Llama.cpp
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In this tutorial, I guide you through the process of building a cutting-edge Medical Retrieval Augmented Generation (RAG) Application using a suite of powerful technologies tailored for the medical domain. I start by introducing BioMistral 7B, a new large language model specifically designed for medical applications, offering unparalleled accuracy and insight into complex medical queries.
Next, I delve into Qdrant, a self-hosted vector database that we run inside a Docker container. This robust tool serves as the backbone for managing and retrieving high-dimensional data vectors, such as those generated by our medical language model.
To enhance our model's understanding of medical texts, I utilize PubMed BERT embeddings, an embeddings model specifically crafted for the medical domain. This ensures our application can grasp the nuances of medical literature and queries, providing more precise and relevant answers.
For orchestrating our application components, I introduce LangChain, an orchestration framework that seamlessly integrates our tools and services, ensuring smooth operation and scalability.
On the backend, I leverage FastAPI, a modern, fast (high-performance) web framework for building APIs with Python 3.7+. FastAPI provides the speed and ease of use needed to create a responsive and efficient backend for our medical RAG application.
Finally, for the web UI, I employ Bootstrap 5.3, the latest version of the world’s most popular front-end open-source toolkit. This enables us to create a sleek, intuitive, and mobile-responsive user interface that makes our medical RAG application accessible and easy to use.
Join me as I walk you through each step of the process, from setting up the environment to integrating these technologies into a cohesive and functional medical RAG application. Whether you're a developer interested in medical applications, a data scientist looking to expand your toolkit, or simply curious about the latest in Gen AI and machine learning, this tutorial has something for you.
Don't forget to like, comment, and subscribe for more tutorials like this one. Your support helps me create more content aimed at exploring the forefront of technology and its applications in the medical field. Let's dive in!
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To further support the channel, you can contribute via the following methods:
Bitcoin Address: 32zhmo5T9jvu8gJDGW3LTuKBM1KPMHoCsW
#mistral #ai #llm
Next, I delve into Qdrant, a self-hosted vector database that we run inside a Docker container. This robust tool serves as the backbone for managing and retrieving high-dimensional data vectors, such as those generated by our medical language model.
To enhance our model's understanding of medical texts, I utilize PubMed BERT embeddings, an embeddings model specifically crafted for the medical domain. This ensures our application can grasp the nuances of medical literature and queries, providing more precise and relevant answers.
For orchestrating our application components, I introduce LangChain, an orchestration framework that seamlessly integrates our tools and services, ensuring smooth operation and scalability.
On the backend, I leverage FastAPI, a modern, fast (high-performance) web framework for building APIs with Python 3.7+. FastAPI provides the speed and ease of use needed to create a responsive and efficient backend for our medical RAG application.
Finally, for the web UI, I employ Bootstrap 5.3, the latest version of the world’s most popular front-end open-source toolkit. This enables us to create a sleek, intuitive, and mobile-responsive user interface that makes our medical RAG application accessible and easy to use.
Join me as I walk you through each step of the process, from setting up the environment to integrating these technologies into a cohesive and functional medical RAG application. Whether you're a developer interested in medical applications, a data scientist looking to expand your toolkit, or simply curious about the latest in Gen AI and machine learning, this tutorial has something for you.
Don't forget to like, comment, and subscribe for more tutorials like this one. Your support helps me create more content aimed at exploring the forefront of technology and its applications in the medical field. Let's dive in!
Join this channel to get access to perks:
To further support the channel, you can contribute via the following methods:
Bitcoin Address: 32zhmo5T9jvu8gJDGW3LTuKBM1KPMHoCsW
#mistral #ai #llm
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